Context-based selection of calls-to-action associated with search results

ABSTRACT

In one embodiment, a method includes receiving a search query for content items of the online social network, identifying content items of the online social network that match the search query, and sending, in response to the search query, search results corresponding to the identified content items, where each search result is presented with one or more call-to-action elements applicable to the content item, and where the call-to-action elements are presented in ranked order based on a suitability of the call-to-action element to the content item.

PRIORITY

This application is a continuation under 35 U.S.C. § 120 of U.S. patent application Ser. No. 13/152,664, filed 3 Jun. 2011.

TECHNICAL FIELD

This disclosure generally relates to improving the quality of search results identified for search queries.

BACKGROUND

The Internet provides a vast amount of information, which may be stored at many different sites and on many different devices, such as on servers and clients or in databases, around the world. These different devices at the different sites are communicatively linked to computer or communication networks over wire-line or wireless connections. A person may access specific pieces of information available on the Internet using a suitable network device (e.g., a computer, a smart mobile telephone, an entertainment console, etc.) connected to a network.

Due to the sheer amount of information available on the Internet, it is impractical as well as impossible for a person (e.g., a network user) to manually search throughout the Internet for the specific pieces of information he needs. Instead, most network users rely on different types of computer-implemented tools to help them locate the desired information. One of the most commonly and widely used computer-implemented tools is a search tool, also referred to as a search engine. To search for information relating to a specific topic on the Internet, a user typically provides a few words, often referred to as a “search query” or simply “query”, describing the topic to a search tool. The search tool conducts a search based on the search query using various search algorithms and generates a set of search results, each corresponding to some information that is most likely to be related to the search query. The search results are then presented to the user.

Sophisticated search tools implement many other functionalities in addition to merely identifying the search results as a part of the search process. For example, a search tool usually ranks the identified search results according to their relative degrees of relevance with respect to the search query, such that the search results that are relatively more relevant to the search query are ranked higher than and consequently are presented to the network user before the search results that are relatively less relevant to the search query. Sometimes, a search result may be associated with a clickable call-to-action icon or button so that the user may click on it to further interact with the search result. There are continuous efforts to improve the quality of the search results generated by the search tools.

SUMMARY OF PARTICULAR EMBODIMENTS

This disclosure generally relates to improving the quality of search results identified for search queries.

In particular embodiments, one or more computing devices may access a search query provided by a user of a social-networking system; identify a set of search results in response to the search query; associate one or more call-to-action elements with one or more search results in the set of search results based on one or more factors associated with the social-networking system; and present the set of search results, including the one or more call-to-action elements, to the user.

These and other features, aspects, and advantages of the disclosure are described in more detail below in the detailed description and in conjunction with the following figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example graph that represents the information contained in a social-networking system.

FIG. 2A illustrates an example user interface through which a user may search for information.

FIGS. 2B-2C illustrates an example user interface where a search result is associated with various call-to-action elements.

FIG. 3 illustrates an example method of constructing a set of search results in response to a search query.

FIG. 4 illustrates an example computer system.

DESCRIPTION OF EXAMPLE EMBODIMENTS

This disclosure is now described in detail with reference to a few embodiments thereof as illustrated in the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of this disclosure. However, this disclosure may be practiced without some or all of these specific details. In other instances, well known process steps and/or structures have not been described in detail in order not to unnecessarily obscure this disclosure. In addition, while the disclosure is described in conjunction with the particular embodiments, it should be understood that this description is not intended to limit the disclosure to the described embodiments. To the contrary, the description is intended to cover alternatives, modifications, and equivalents as may be included within the spirit and scope of the disclosure as defined by the appended claims.

A computer-implemented search tool is designed to search for information relevant to specific topics on one or more networks, such as the Internet or an Intranet. To conduct a search, a network user may issue a search query to the search tool. The search query generally contains one or more words that describe a topic. In response, the search tool may identify a set of search results, each corresponding to some information that is likely to be related to the search query. The set of search results may be ranked based on any number of factors and presented to the user according to their respective ranks.

When ranking a set of search results with respect to a search query, many different factors may be considered. For example, the content of each search result may be analyzed to determine its degree of relevance to the search query. In addition, particular embodiments may rank the search results based on factors such as, for example and without limitation, the context in which the search is being conducted, the nature or characteristics of the topic described by the search query, the time when and the location where the search is requested, and the social-networking information and the behavior profile of the user requesting the search.

Particular embodiments may also boost the ranks of certain search results according to objectives such as, for example and without limitation, the business strategies or goals of the entity providing the search tool. In particular embodiments, the search tool may be provided by or associated with a social-networking system. The social-networking system may have any number of features, such as, for example and without limitation, enabling its users to contact their friends via emails or messages, organize events, form social groups, upload and share photos, read news feeds, use various web-based applications (e.g., provided either by the social-networking system or third parties), play online games, and so on. In particular embodiments, one or more search results in the set may be associated with such a feature. When ranking the set of search results, the level of interaction the user has with the feature may be taken into consideration. In some cases, the respective ranks of the search results associated with the feature may be boosted (i.e., artificially increased) in order to bring the feature to the user's attention in the hopes that the level of interaction the user has with the feature may be increased.

In particular embodiments, one or more search results in the set may be associated with various call-to-action elements. The term “call to action”, in the context of network content and more specifically, in the context of user experience, is a user-interface element that prompts an action from a user to initiate some function or process, such as initiating a call or other communication. The most popular manifestation of call to action in web-based interfaces is in the form of clickable icons or buttons, which, when clicked, perform specific actions or lead to specific network content. In particular embodiments, a search result may be associated with a call-to-action element (e.g., an icon or button) so that a user may click on it to further interact with the search result. Sometimes, multiple types of call-to-action elements may be appropriately associated with a specific search result. Particular embodiments may select one of the call-to-action elements for the search result based on various factors, such as, for example and without limitation, the context in which the search is being conducted, the nature or characteristics of the topic described by the search query, the content of the search result, the time when and the location where the search is requested, the social-networking information and the behavior profile of the user requesting the search, and the business strategies or objectives of the entity (i.e., the social-networking system) providing the search tool.

A social network, in general, is a social structure made up of entities, such as individuals or organizations, that are connected by one or more types of interdependency or relationships, such as friendship, kinship, common interest, financial exchange, dislike, or relationships of beliefs, knowledge, or prestige. In more recent years, social networks have taken advantage of the Internet. There are social-networking systems existing on the Internet in the form of social-networking websites. Such social-networking websites enable their members, who are commonly referred to as website users, to perform various social activities. For example, the social-networking website operated by Facebook, Inc. at www.facebook.com enables its users to communicate with their friends via emails, instant messages, or blog postings, organize social events, share photos, receive news of their friends or interesting events, play games, organize events, etc.

A social-networking system may contain a vast amount of information related to its users. Such information is not limited to the social connections of the individual users, but may include, for example and without limitation, demographical information, network or social activities, behavior profiles, and personal preferences, interests, or hobbies of the individual users. Particular embodiments may represent the information contained in a social-networking system using a graph that may have any number of nodes and edges, an example of which is illustrated in FIG. 1.

In graph 100 illustrated in FIG. 1, each node may represent an entity, which may be human (e.g., user of the social-networking system) or non-human (e.g., location, event, action, business, object, message, post, image, web page, news feed, etc.). Two nodes are connected with an edge if the two nodes are related in some way (i.e., there is a relationship between the two nodes). Example cases when two nodes in graph 100 may be related and thus connected with an edge may include, without limitation, (1) the two nodes represent two users of a social-networking system respectively, and the two users are socially connected (e.g., friends of each other); (2) the two nodes represent a user of the social-networking system and an event respectively, and the user has attended the event; (3) the two nodes represent a user of the social-networking system and a location, and the user has been to the location; (4) the two nodes represent a user of the social-networking system and the user has interacted with (e.g., viewed) the web page; (5) the two nodes represent an event and a location respectively, and the event is held at the location; (6) the two nodes represent a user of the social-networking system and an image (e.g., a digital photograph) respectively, and the user is in the image; (7) the two nodes represent a user of the social-networking system and a product (e.g., a mobile telephone) respectively, and the user owns and uses the product; and (8) the two nodes represent a user of the social-networking system and a software application (e.g., a web-based game) respectively, and the user uses the application (e.g., plays the game). A connection may exist between two humans, a human and a non-human entity, and two non-human entities. Any type of relationship between two human or non-human entities may result in a connection between the two entities.

In graph 100, when there is an edge between two specific nodes, the two nodes may be considered directly related. For example, edge 120A connects nodes 110A and 110B, and thus nodes 110A and 110B are directly related. Similarly, edge 120B connects nodes 110B and 110C, and thus nodes 110B and 110C are directly related. When there is no edge between two particular nodes, the two nodes may still be considered indirectly related. For example, there is no edge directly connecting nodes 110A and 110C; however, nodes 110A and 110C may still be considered indirectly related through node 110B.

With respect to node 110A, node 110B has a closer relationship to it than node 110C, because in graph 100, it takes one hop to go from node 110A to node 110B, but it takes two hops to go from node 110A to node 110C (i.e., through node 110B). In particular embodiments, with respect to two specific nodes, the less number of hops it takes to go from one node to another node, the closer the two nodes are related.

In particular embodiments, a social-networking website implementing a social-networking system may provide a search tool that enables its users to search for information relating to specific topics in the social context. The information represented by a graph, such as the one illustrated in FIG. 1, may be used to help identify and rank the search results. FIG. 2A illustrates an example user interface 200 through which a user may provide search queries and receive search results. In this case, there is an input field 210 through which a user may provide search queries (e.g., search query 240), an icon 220 through which the user may submit the search queries (e.g., by clicking on icon 220), and an output field 230 where the search results (e.g., search results 242A-E) may be displayed. In addition, sometimes, a search result may be associated with a call-to-action element. For example, in FIG. 2A, search result 242A is associated with call-to-action icon 244A; search result 242D is associated with call-to-action icon 244D; and search result 242E is associated with call-to-action icon 244E. Note that it is not necessary that each and every search result in a set is associated with a call-to-action element. For example, in FIG. 2A, search results 242B and 242C are not associated with any call-to-action elements. Call-to-action icons 244A, 244D, and 244E may be clickable icons. If a user clicks on one of these call-to-action icons, the user may further interact with the associated search result or an action may be performed in connection with the associated search result. In particular embodiments, a call-to-action element may be presented in close proximity to its associated search result.

In particular embodiments, multiple call-to-action elements may be associated with the same search result. For example, if the search result is an email message, then the call-to-action elements associated with the email message search result may include “reply”, “forward”, and “delete”. If the search result is a restaurant, then the call-to-action elements associated with the restaurant search result may include “check-in”, “like”, and “make reservation”. Some search results may be personally or professionally related to the user requesting the search (e.g., search results that are personal or professional contacts obtained from the user's address book). If the search result is a friend of the user's, the call-to-action elements may include “make phone call”, “send email”, “send SMS”, “start chart session”, “post to wall”, etc. As these examples illustrate, what call-to-action elements are suitable for a particular search result may depend on the nature of the search result itself.

In particular embodiments, each search result 242 may be associated with an expansion icon 252 (e.g., a downward triangle), as illustrated in FIG. 2B. When the user clicks on an expansion icon 252, the call-to-action elements associated with the corresponding search result 242 are displayed. Suppose that the user clicks on expansion icon 252B. In FIG. 2C, three call-to-action elements 244B associated with search result 242B are displayed. In addition, expansion icon 252B, associated with search result 242B, is replaced with a contraction icon 254B (e.g., an upward triangle). When the user clicks on a contraction icon, the call-to-action elements associated with the corresponding search result 242 are hidden.

In particular embodiments, a user interface, such as the one illustrated in FIGS. 2A-2C, may be incorporated in a web page or a window panel for display on the screen of an electronic device. For example, interface 200 may be displayed on the screen of a mobile telephone or tablet.

In particular embodiments, the search tool may implement the “type ahead” feature, also known as “incremental search”, “incremental find”, or “real-time suggestions”, which is a user interface interaction method to progressively search for and filter through texts (e.g., search queries). As a user types the text of a search query, one or more possible matches for the text are found and immediately presented to the user. This immediate feedback often allows the user to stop short of typing the entire word or phrase of the search query. The user may also choose a closely related option from the presented list. In addition, in particular embodiments, as the user types each character of a search query, a set of search results corresponding to the text thus far typed may be presented to the user immediately. The search results may be updated each time the user further types a character.

For example, suppose that a user wants to search for a person, and begins to input the person's name as the search query. The user first types the character “j” (e.g., in an input field included in a user interface). At this point, some of the names that begin with the letter “j” (e.g., Jane, John, Joseph, Judith, etc.) may be suggested to the user. In addition, a set of search results corresponding to one or more of the suggested names (e.g., the first suggested name, Jane, or several of the suggested names, Jane, John, Joseph, etc.) may be presented to the user as well. Suppose that the user next types in the character “o”. At this point, some of the names that begin with the letters “jo” (e.g., Joan, John, Jordon, Joseph, Joshua, etc.) may be suggested to the user. In addition, a set of search results corresponding to one or more of the suggested names (e.g., Joan, or Joan, John, etc.) may be presented to the user as well. This process may continue until the user finishes typing the name or selects one of the suggested names or search results. Type ahead is described in more detail in U.S. patent application Ser. No. 12/816,377, entitled “Search and Retrieval of Objects in a Social Networking System”, filed on 15 Jun. 2010, which is hereby incorporated by reference in its entirety and for all purposes.

When typing search terms, the user can occasionally enter wrong characters as part of the search phrase. Using past knowledge of the particular user's search queries, general user search queries, the layout of the current keyboard on the device, and a definition of the language or languages the user is likely to be typing in, allows the search provider to “guess” the likely search phrase even when an incorrect character has been entered. For example, the character “o” on a standard US QWERTY keyboard is most closely surrounded by the characters “o”, “p”, “;”, “,”, and “k”. If a user types “j”, then the set of results for all 2-letter combinations other than “jo” are unlikely to be the user's intended search query. If results exist for “jl”, they can be displayed. If no such results exist, then the results for “jo” can be displayed to the user, with an indicator that an auto-correction has temporarily been applied. This reduces the need for the user to correct the search query before tapping on the search result.

Note that in the example user interface 200 illustrated in FIGS. 2A-2C, an icon 220 is illustrated for submitting the search queries. This icon is not necessary in all cases. For example, when the “type ahead” feature is supported, icon 220 may not be needed and included in a search-related user interface.

When a search tool receives a search query from a user, it may identify a set of search results considered relevant to the search query. In addition, the set of search results may be ranked based on various factors, objectives, and other considerations. The search results that are ranked higher are presented to the user before the search results that are ranked lower. Achieving a good ranking of the search results for a user is especially desirable in some cases. For example, often, a user may conduct searches using a mobile electronic device (e.g., a mobile telephone or a tablet computer) that has a relatively small display screen. Consequently, not many search results may be displayed and presented to the user within one screen. To avoid having to make the user scroll down multiple screens to locate the specific search results he is looking for, if these search results are ranked higher and presented to the user first, it can save the user time and make the search experience more efficient and pleasant for the user.

In some cases, certain search results may be associated with various call-to-action elements. Similarly, given a specific search result, it may be desirable to select an appropriate call-to-action element (e.g., icon or button) to be associated with the search result so that the user may conveniently select the call-to-action element, which is often presented in close proximity to the associated search result, to perform an action with respect to the search result. Again, a user may conduct searches using a device (e.g., a mobile device) where it may not be easy and fast to navigate among the different user-interface elements. To avoid having to make the user perform several interactions with the device in order to initiate the desired action with respect to a search result, if the appropriate call-to-action element is presented near the associated search result, the user may easily select that call-to-action element to initiate the desired action with respect to a search result.

In particular embodiments, the user may be a user of the social-networking system providing or associated with the search tool. When compiling and ranking the set of search results in response to a search query, the search tool may examine and analyze any relevant information available to the social-networking system and thus to the search tool (i.e., not just limited to web pages or other types of contents available on the Internet). For example and without limitation, the search tool may consider: (1) the nature and the context (especially the social context) of the search query (e.g., whether the search query refers to a person, a location, an event, a place, a software or web application, an object, etc.); (2) the social and demographical information of the user requesting the search (e.g., the user's social connections, age, gender, family status, etc.); (3) the behavior profile or activities of the user (e.g., the user's daily routines, hobbies, interests, past network or social activities, etc.); (4) the time when and the location where the user provides the search query; (5) whether the social-networking system desires to promote certain search results (e.g., search results associated with a feature of the social-networking system, which the social-networking system desires to bring to the user's attention); (6) whether the social-networking systems desires to train or guide the user toward certain search results or behaviors; (7) the historical behavior of the user in using this or other search tools previously, such as whether the user typically selects or fails to select certain classes of or certain particular results; (8) the historical behavior of the user in certain locations (e.g., the user always selects results referring to people while at home, but selects results referring to businesses while not at home); and (9) the historical behavior of the user with respect to whether the user responds to the search results after selecting one (e.g., by commenting or liking on the result that is delivered). In addition, in particular embodiments, the search tool may examine and analyze information stored on the electronic device used by the user to request the search, and may include some of such information in the search results (e.g., if such information is considered relevant to the search query).

FIG. 3 illustrates an example method of constructing a set of search results in response to a search query while taking into consideration at least some of the social information relevant to the user requesting the search. As described above in connection with FIG. 1, the information available in a social-networking system may be represented by a graph (e.g., graph 100 illustrated in FIG. 1). Such a graph may be used when compiling and ranking a specific set of search results for a specific user, as well as when selecting call-to-action elements for specific search results. In particular embodiments, since the user requesting the search is a user of the social-networking system, one of the nodes in the graph associated with the social-networking system may represent the user. By analyzing the graph, the search tool may determine what other human and non-human entities are directly or indirectly related to the user (e.g., other nodes in the graph that are connected, directly or indirectly, to the node representing the user), the types of these relationships (e.g., what relationships the edges represent), and how closely the other nodes are related to the node representing the user (e.g., the number of hops between each pair of nodes).

Particular embodiments may receive a search query from a user, as illustrated in STEP 310. The user may be a user of the social-networking system and may provide the search query through an electronic device (e.g., a mobile telephone or tablet) connected to a network. For example, suppose that the search query is the word “Johnson”.

Particular embodiments may identify and compile a set of search results in response to the search query, as illustrated in STEP 320. The search results may include any type of information or content. For example, the word “Johnson” may refer to a person (e.g., Mary Johnson), a location (e.g., Johnson City), an establishment (e.g., Johnson's Hardware Store, or Johnson BBQ Restaurant), a brand or product name (e.g., Johnson & Johnson), a news story (e.g., a news feed about Jimmie Johnson), and a message (e.g., a post by or concerning Tom Johnson). The search results may be a mixture of many types of information considered relevant to the search query.

When selecting the search results, particular embodiments may obtain information from various sources, such as, for example and without limitation, information stored locally on the electronic device used by the user, information stored on servers or in databases associated with the social-networking system, and information publically available on the Internet. For example, the contact information of a person named “Mary Johnson” may be stored locally on the user's electronic device. Thus, the name and contact information (e.g., email address or phone number) of Mary Johnson may be selected as one of the search results included in the set. In this case, the search result comes from the information stored on the electronic device used by the user when conducting the search. As another example, from the user's account at the social-networking website, it may be determined that the user has visited a restaurant called “Johnson BBQ Restaurant” several times in the past (e.g., the user has “checked in” with the social-networking system when at Johnson BBQ Restaurant). Thus, the name and address of Johnson BBQ Restaurant may be selected as one of the search results included in the set. In this case, the search result comes from the information stored on servers or in databases associated with the social-networking system. As another example, there may be news stories on the Internet about Jimmie Johnson participating in a NASCAR race. Thus, a web page containing such a news story may be selected as one of the search results included in the set. In this case, the search result comes from the information publically available on the Internet.

When the user submits the search query to initiate the search process, since the search results may be obtained from various sources, some specific search results may become available before others. For example, search results obtained from information stored locally on the electronic device used by the user may be readily available, before search results obtained from information associated with the social-networking system or on the Internet, which may take a slightly longer time to obtain since they are stored at remote sites. In particular embodiments, as soon as some search results become available, they may be presented to the user immediately, instead of having to wait for other search results to become available. For example, in the above scenario, the contact information for “Mary Johnson” is stored locally on the user's electronic device and thus may be readily available. Upon the user submitting the search query “Johnson”, the contact information for “Mary Johnson” may be presented to the user immediately. Then, as more search results become available (e.g., obtained from the social-networking system or the Internet, such as the address and phone number of “Johnson BBQ Restaurant”), they may be added to the set of search results compiled for the search query “Johnson” and presented to the user accordingly (e.g., upon their availability).

A social-networking system may implement and support any number of features. In particular embodiments, one or more of the search results selected in response to the search query may be associated with one or more of such features. In some cases, it may be possible that a search result not highly relevant to the search query may nevertheless be selected and included as one of the search results for the search query. For example, one feature may be that a user may play online games through the social-networking website. One of the available games called “Hatchlings” may be selected as one of the search results for search query “Johnson”, even though this game has little or no apparent relevance to the word “Johnson”.

Particular embodiments may rank the set of search results based at least on the social context or information relevant to the user requesting the search, as illustrated in STEP 330. Existing search engines (e.g., search engines provided by Google, Microsoft, or Yahoo) rank search results based mainly on content relevance. They do not take into consideration of social context or information relevant to the specific user requesting the search because they are not provided by any social-networking system that possesses such social information. Instead or in addition to content relevance, particular embodiments may take into consideration of the social information available to the social-networking system providing or associated with the search tool as some of the factors when ranking the search results for the user. In a sense, the ranking is customized for each individual user based on the user's social information. In addition, in particular embodiments, the user's past behavior may be used to help identify and/or rank the search results.

For example, suppose that there are three people named “Johnson” found as users of the social-networking system. Without any social information of the user requesting the search, all three Johnsons may be similarly likely to be the person the user is searching for. In one case, suppose that based on the social information of the user, one of the Johnsons (e.g., Johnson #1) may be a friend of the user while the other two are not. In this case, Johnson #1 may be ranked higher than the other two Johnsons as it is more likely that the user is searching for his friend instead of some stranger. Furthermore, the other two Johnsons may not even be selected as search results for the user. In another case, suppose that two Johnsons (e.g., Johnson #1 and Johnson #2) are both friends of the user, but Johnson #1 is directly connected to the user (e.g., in the graph, there is an edge between the two nodes representing the user and Johnson #1 respectively) while Johnson #2 is indirectly connected to the user (e.g., connected through one or more other nodes based on the graph), or the user has contacted Johnson #1 more times or more frequently than Johnson #2. In this case, both Johnson #1 and Johnson #2 may be included in the set of search results, but Johnson #1 may be ranked higher than Johnson #2.

As another example, suppose that there are two businesses having the word “Johnson” in their names: Johnson's Hardware Store and Johnson BBQ Restaurant. The electronic device the user uses to conduct the search may supply location data (e.g., GPS data) that indicate the location of the device, and thus the location of the user, where the user requests the search on “Johnson”. In one case, suppose that Johnson BBQ Restaurant is near the location of the user, while Johnson's Hardware Store is farther away. In this case, Johnson BBQ Restaurant may be ranked higher than Johnson's Hardware Store as it is more likely that the user is searching for a nearby business. In another case, suppose that the user has visited Johnson BBQ Restaurant in the past but has never been to Johnson's Hardware Store. In this case, Johnson BBQ Restaurant may also be ranked higher than Johnson's Hardware Store as it is more likely that the user is searching for a business with which he has some contact in the past.

As a third example, suppose that there are two events having the word “Johnson” in their descriptions: Johnson's birthday party and Johnson City counsel meeting. If the user has been invited to Johnson's birthday party and has responded affirmatively to the invitation, then Johnson's birthday party may be ranked higher than Johnson City counsel meeting. If the user does not live in Johnson City, then Johnson City counsel meeting may not be included in the search results at all, as Johnson City counsel meeting probably has no relation to the user. If Johnson's birthday party is scheduled for Saturday evening at 6:00 pm and the time the user requests the search is approximately Saturday at 5:00 pm, then again, Johnson's birthday party may be ranked higher, as it is more likely that, as the time when the user conducts the search is fairly close to the time of the event, the user is checking for information about the event in preparation of attendance.

As a fourth example, suppose that based on the user's profile, the user is interested in auto racing, or the user has posted messages about auto racing. In one case, a news story (e.g., a news feed) about Jimmie Johnson participating in a NASCAR race may be ranked higher as it is more likely that the user may be interested in such a story. In another case, suppose that there are multiple news feeds about Jimmie Johnson and auto racing, but some are older and some are newer. The more recent news feeds that are closer to the time when the user conducts the search may be ranked higher than the older news feeds as they are more likely to be of interest to the user than the older, and thus perhaps obsolete, news feeds.

As the above examples illustrate, when selecting and ranking a set of search results in response to a search query submitted by a user, any available information relevant to the user, to the search, or to the search query may be used. Such information may be stored at various sites, such as on the user's device, in association with the social-networking system, or publicly on the Internet. If some information is already available on the user's device, then it may not be necessary to obtain that information again from the social-networking system or on the Internet. In particular embodiments, the information contained in the social-networking system may be represented as a graph (e.g., as illustrated in FIG. 1), and such a graph may be used to compute, for each node in the graph, a coefficient that indicates its relationship to the node corresponding to the user requesting the search. This coefficient may be used to help rank the set of search results. In particular embodiments, each search result may correspond to a node in the graph as well.

In particular embodiments, a specific search result may be presented to the user as soon as it becomes available. As more search results become available, they may be added to and co-mingled with the existing search results and presented to the user as well. At the same time, the search results may be ranked and the higher-ranked search results should be presented to the user before the lower-ranked search results. However, a first search result that becomes available before a second search result is not necessarily ranked higher than the second search result. In particular embodiments, when ranking the search results, a ranking score may be computed for each search result, and the search results are ranked based on their respective ranking scores. In particular embodiments, as each search result becomes available, its ranking score may be computed. Between two search results, suppose that the first search result becomes available before the second search result; however, the ranking score of the first search result is lower than the ranking score of the second result, which means that based on their respective ranking scores, the second search result should be presented above the first search result on the display screen of the user's electronic device. By the time the second search result becomes available, the first search result may have already been presented to the user (i.e., displayed on the display screen of the user's electronic device). In particular embodiments, since the second search result is ranked higher than the first search result, when presenting the second search result to the user, the second search result is placed (e.g., inserted) above the first search result on the display screen of the user's electronic device, effectively “pushing” the first search result further down the display screen.

Optionally, particular embodiments may boost the respective ranks of one or more search results in the set so that they may be presented to the user before some other search results that would normally rank higher than them. There are different reasons for boosting the rank of a specific search result.

For example, the social-networking system providing the search tool may wish to achieve certain business objectives. Suppose that the user requesting the search only has a very few friends (e.g., less than 5 friends) identified in his account at the social-networking website. The social-networking system may desire to encourage such a user to have more friends. Suppose that the user is attending an event at a specific location when he conducts a search based on the query “Johnson”. In one case, suppose that there are two Johnsons also attending the same event. Johnson #1 is the user's friend, while Johnson #2 is a stranger to the user. Normally, Johnson #1 would be ranked higher than Johnson #2, as it is more likely that the user is searching for his friend who is attending the same event as he is. However, to encourage the user to meet new friends, the rank of Johnson #2 may be booted to be above the rank of Johnson #1, and so, Johnson #2 is presented to the user before Johnson #1. As a result, the user may meet and become friends with Johnson #2 as well. In another case, suppose that a person named “Smith”, who is a friend to the user's friend Johnson but is a stranger to the user, is also attending the same event. Normally, Smith may not be included in the set of search results compiled for the query “Johnson”, or if Smith is included in the search results compiled for the query “Johnson”, it may be ranked relatively low. However, to encourage the user to meet new friends, the rank of Smith may be boosted to be above the ranks of some other search results, and so is presented to the user fairly soon. As a result, the user may meet and become friends with Smith, especially considering they share a common friend.

As a second example, the social-networking system may wish to train the user about certain functionalities of features, or encourage certain user behaviors. In one case, suppose that the user requesting the search does not yet know he can play various online games through the social-networking website. When the user requests a search for the query “café”, an online game named “Café Mystery” may be included in the search results with a boosted rank. When this game is presented to the user, the user may learn that he can play this game and many other games. Such type of boosting may be repeated for a number of times, until the user is familiar with the functionalities or acquired certain behavior patterns. Thereafter, it would no longer be necessary to boost the ranks of similar types of search results.

As a third example, suppose that the social-networking system recently has added a new feature to its website. To bring this new application to its users' attention, when a user requests a search, a search result relating to the new feature (e.g., a link pointing to the new feature) may be included as one of the search results. This may be determined based on whether a specific user has previously used the new feature. In one case, if the user requesting the search has already used the new feature, then the new feature may not need to be included in the search results, especially if the new feature has little or no relevance to the search query, as the user already knows about the new feature. On the other hand, if the user requesting the search has never interacted with the new feature thus far, then the new feature may be selected as one of the search results. Furthermore, the ranking of the new feature may be boosted so that it is presented to the user fairly soon to capture the user's attention.

In particular embodiments, given a set of search results compiled for a search query provided by a user, a ranking score may be computed for each search result in the set. In particular embodiments, the ranking score may be computed using an algorithm that takes into consideration of many factors (e.g., various factors as inputs to the algorithm), such as, for example and without limitation, the level of content relevance of the search result to the search query, the level of social relevance of the search result to the user requesting the search or the search query, the amount of boosting given to the search result, if any (e.g., determined based on business objectives), the degree of closeness in terms of time or location the search result has to the time when or location where the user requests the search, and so on. The algorithm may combine all available factors (i.e., inputs) to determine a final ranking score for each search result. In particular embodiments, the ranking score may be normalized to a number between 0 and 1.

In some cases, there may be one or more search results associated with a feature of the social-networking website included in the set. Given a specific feature, in particular embodiments, if the user has not interacted with the feature, then one or more search results associated with the feature may be included in the set. Furthermore, the ranking of these search results may be boosted (e.g., by providing a boosting factor to the ranking algorithm that increases the respective ranking scores of these search results) so that these search results are presented to the user sooner, in the hopes that the user may become aware of and subsequently use the feature. On the other hand, if the user has already interacted with the feature, then no special action in connection with the feature (e.g., selecting search results associated with the feature and boosting their ranking) may be needed.

Alternatively, in particular embodiments, given a specific feature, the amount or frequency of interaction the user has with the feature may be determined. For example, the level of interaction the user has with the feature may be represented as a number between 0 and 1. If the user has no interaction with the feature, the level of interaction may be 0. On the other hand, if the user uses the feature often, the level of interaction may be close to 1. When a user requests a search to be conducted on a given search query, whether to include one or more search results relating to the feature in the set of search results compiled for the search query and if so, how much to boost the ranking of these search results may depend on the current level of interaction the user has with the feature. For example, if the user has no or very low level of interaction with the feature, several search results relating to the feature may be included in the set and their rankings may be boosted much higher. As the user interacts with the feature more and more, and number of search results relating to the feature included in the set may gradually decrease and their rankings may only need to be boosted slightly. Finally, as the user interacts with the feature frequently, no search result relating to the feature needs to be included in the set.

In particular embodiments, a boosting coefficient (e.g., a number between 0 and 1) may be determined for each search result relating to the feature based on the current level of interaction the user has with the feature. This boosting coefficient may be supplied to the ranking algorithm as one of the inputs when computing the ranking score for each search result. For a search result not related to the feature, its boosting coefficient may be set to 0 (i.e., no boosting). For example, when the user has no or little interaction with the feature, the boosting coefficient may be close to 1. As the user interacts with the feature more and more, the boosting coefficient may gradually decreases. Finally, as the user interacts with the feature frequently, the boosting coefficient may be close to 0.

In particular embodiments, when including search results relating to a feature in a set of search results compiled for a search query, the user may be given an option to indicate that he is not interested in that feature. If the user has indicated that he is not interested in a specific feature, then for subsequent searches requested by this user, search results relating to the specific feature are not artificially selected as search results, especially when such search results have little or no relevance otherwise to the search queries provided by the user. Thus, once the user has indicated that he is not interested in a specific feature, even if the user has never interacted with the feature, search results relating to the feature are not artificially selected as search results, and when search results relating to the feature are selected as search results, their respective ranks are not artificially boosted.

In some cases, the user's past behavior, when available, may be used to help select and/or rank the search results, as well as determine which search results should have their ranks boosted. In particular embodiments, the user's behavior may be collected into a behavior profile, and the information contained in this profile may be used by the search tool as needed. For example, suppose that a user rarely or never selects (e.g., taps) on search results that are places. This may suggest that the user is not interested in place-type search results. In this case, to take into consideration the user's personal preference, when selecting search results for the user, the search tool may include very few or no place-type search results. Conversely, the system may wish to encourage the user to explore different types of search results in order to expand the user's interests. As a result, when selecting search results for the user, the search tool may include a larger number of place-type search results. Furthermore, the ranks of at least some of the place-type search results may be artificially boosted to bring them to the user's attention. Similar strategies may be applied with respect to a specific search result (e.g., the user never selects a specific search result).

As another example, suppose that the user only selects specific types of search results while at home, at work, or traveling. Thus, based on the user's current location, a larger number of those specific types of search results may be included, and if appropriate, the ranks of at least some of those types of search results may be boosted.

As a third example, the user may like to view (e.g., read) web pages associated with another specific person (e.g., that person's personal page or pages authored by that person, such as blogs), but due to various reasons, the user may not communicate directly to that person (e.g., reply or comment on the pages the user has read). The social-networking system associated with the search tool may have a business object to encourage its users not only to passively view information (e.g., content of web pages) but actively contribute information as well (e.g., by writing comments, replies, etc.). When the user views a web page, it is more likely that the user may be willing to further interact with that page or with the author of the page, since the user has already shown interest in the content of the page. In this case, for example, the ranking of the web page may be boosted.

Particular embodiments may select call-to-action elements for specific search results in the set, as illustrated in STEP 340. In some cases, each and every search result in the set may have an associated call-to-action element, while in other cases, only specific search results in the set may have associated call-to-action elements. Whether a search result has an associated call-to-action element, and if so, the specific type of call-to-action element may depend on many factors, and this disclosure contemplates any applicable factors. For example and without limitation, the factors may include the user's historical behaviors, the time when the search result is presented to the user, the location of the user device (and thus the user) where the search result is presented to the user, what the social-networking system associated with the search tool may encourage or desire the user to do with the search result, the information represented by the graph (e.g., graph 100 illustrated in FIG. 1) associated with the social-networking system, etc.

There are many types of call-to-action elements, such as, for example and without limitation, sending an email, making a telephone call, responding to a message, posting a comment, liking a search result, executing a web-based application, purchasing a product, viewing the content of a search result, and so on. In fact, any action that may be performed with respect to a search result may result in a corresponding call-to-action element. Thus, this disclosure contemplates any applicable type of call-to-action element.

Sometimes, a search result may only be associated with one type of suitable or applicable call-to-action element. In such cases, that type of call-to-action element is likely to be associated with the search result. However, more often, a search result may be associated with multiple types of call-to-action elements. In such cases, one specific type of call-to-action element may be selected for and associated with the search result. What specific types of call-to-action elements are suitable or applicable to a particular search result may depend on the nature or content of the search result itself.

Given a search result that may be associated with multiple call-to-action elements, particular embodiments may select one or more call-to-action elements that are considered most suitable or preferred for the user at the time the search result, together with the call-to-action element(s), is presented to the user. Note that different call-to-action elements may be considered suitable for different users or at different times. In particular embodiments, for a given search result, a set of call-to-action elements suitable or applicable to the search result may be compiled based on available information. The call-to-action elements may then be ranked based on the various factors (e.g., the factors described above). In other words, to determine the appropriate call-to-action element(s) for a search result, particular embodiments may conduct a search (i.e., a second search for call-to-action elements in response to the search result) to compile a set of suitable call-to-action elements for the search result, and then rank the call-to-action elements in the set. The top-ranked call-to-action element or elements may then be associated with the search result. In particular embodiments, the ranking of the call-to-action elements may indicate the degrees of suitability of the individual call-to-action elements to the search result, taking into consideration, for example, the user requesting the search, the time when and/or location where the search is request, the context of the search, etc.

In particular embodiments, when selecting and/or ranking the call-to-action elements for the search results, the information represented by the graph associated with the social-networking system (e.g., graph 100 illustrated in FIG. 1) may be used. As described above, since the user is a member of the social-networking system, one of the nodes in the graph may correspond to the user. In addition, a search result may also correspond to a specific node in the graph. Thus, the relationship or relationships between the user and the search result may be determined by examining how the two nodes corresponding to the user and the search result, respectively, may be related (e.g., directly or indirectly) in the graph.

For example, suppose that a search result identified for the search query “Johnson” is “Mary Johnson”, who is a friend of the user requesting the search. Further suppose that Mary Johnson has several pieces of contact information available, including a home phone number, a work phone number, a mobile phone number, a fax number, an email address, and an instant message address. Thus, the call-to-action elements suitable for Mary Johnson may include “call home”, “call work”, “call mobile”, “send fax”, “send email”, and “send instant message”. These different call-to-action elements may be compiled into a set for Mary Johnson. To rank the call-to-action elements in the set, the user's past communication interactions with Mary Johnson may be analyzed. If the user most frequently calls Mary Johnson's mobile phone when contacting her, then the call-to-action element “call mobile” may be considered most suitable and selected to be associated with the search result “Mary Johnson”. On the other hand, if the user most frequently sends Mary Johnson instant messages when contacting her, then the call-to-action element “send instant message” may be considered most suitable and selected to be associated with the search result “Mary Johnson”. As this example illustrates, when ranking the set of call-to-action elements, the user's relationship with the search result “Mary Johnson” (i.e., social friends) as well as the user's past behavior pattern when interacting with the search result “Mary Johnson” may be relevant factors taking into consideration.

Alternatively, suppose that Mary Johnson is not a personal friend of the user's but a celebrity figure (e.g., an actress) whom the user is interested in. Because the relationship between the user and Mary Johnson is different from the above example, when Mary Johnson is identified as a search result for the search query “Johnson”, the call-to-action elements suitable for the search result “Mary Johnson” may also differ. In this example, the user may not have Mary Johnson's contact information. Instead, the applicable call-to-action elements may include “read headline news about Mary Johnson”, “buy tickets to Mary Johnson's latest movie”, “view Mary Johnson's photos”, “go to Mary Johnson's official website”, and “join Mary Johnson fan club”. If the user has often read news stories about Mary Johnson, then the call-to-action element “read headline news about Mary Johnson” may be considered the top-ranked call-to-action element and associated with the search result “Mary Johnson”. On the other hand, if there is a new movie featuring Mary Johnson released on the day when the user requests the search, then the “buy tickets” call-to-action element may be ranked higher and associated with the search result “Mary Johnson”. In this case, the time when the user requests the search may affect the ranking of the call-to-action elements.

In some cases, the objectives of the social-networking system associated with the search tool may affect the selection and/or ranking of the call-to-action elements. As a third example, suppose that the social-networking system wishes to promote a new web-based application sponsored by the social-networking system at its website. Thus, this web-based application may be included in the set of search results identified for the search query. In addition, the ranking of the web-based application may be boosted, as described above. The call-to-action element selected for and associated with the web-based application may be “launch application” so that the user may conveniently begin to use the web-based application by clicking on the “launch” call-to-action element displayed next to the web-based application. As this example illustrates, a call-to-action element's ranking may be artificially boosted, in a manner similar to boosting the ranking of a search result, as described above. In other words, sometimes, the provider of the search tool (e.g., the social-networking system) may prefer specific call-to-action elements (e.g., based on its business objectives) and thus may choose to boost the ranks of its preferred call-to-action elements over other call-to-action elements so that these call-to-action elements preferred by the service provider may be selected to be associated with the search results over the other call-to-action elements.

In some cases, the time when the search is conducted and/or the location where the user is when requesting the search may affect the selection and/or ranking of the call-to-action elements. For example, suppose that a search result is “Johnson BBQ Restaurant”. Suitable call-to-action elements that may be associated with Johnson BBQ Restaurant may include “check in”, “like”, and “make reservation”. If the user is currently at Johnson BBQ Restaurant (e.g., as indicated by the location data provided by the user's device) when the user requests the search, then “check in” may be selected to be associated with the search result, as it is likely that the user is attending a dinner party at the restaurant and wishes to check in. Of course, multiple factors may together indicate which specific call-to-action element is more suitable for a given user at a given time and/or location. Thus, suppose that based on the user's calendar, it may be determined that: (1) there is a dinner party held at Johnson BBQ Restaurant; (2) the user has been invited to the dinner party (i.e., the user is an invitee); (3) the user has agreed to attend the party (e.g., the user has RSVP'ed); and (4) the current time when the user requests the search is around the time of the dinner party. All these pieces of information may be combined to determine the ranks of the call-to-action elements for the search result.

As another example, suppose that the user is playing a computer game (e.g., web based or desktop based) with other people. Thus, when the web-based game is a search result, the suitable call-to-action elements that may be associated with the game may include input actions (e.g., designed by the game's developer) that the user may take with the game. Suppose that the game is an interactive game between multiple players (e.g., the players fight against each other). Based on the game scenario, a player may kick or punch an opponent, dodge or block an attack from an opponent, etc. Thus, the call-to-action elements that may be associated with the game may include “kick”, “punch”, “dodge”, and “block”. In particular embodiments, the call-to-action elements associated with the web-based game may ranked while taking into consideration the current state of the game with respect to the user. For example, if last action occurred with respect to the user in the game is another player kicking the user, then the “block” call-to-action element may be ranked higher and associated with the game, as under the circumstances, the user is likely to take a defensive action against the attack.

In particular embodiments, the call-to-action associated with a search result may be updated as the circumstances change. In the above example, once the user has clicked on the “block” call-to-action element to block the kick from the other player, the call-to-action element associated with the game may be updated to “kick” so that the user may now make a counter-attack move against the other player.

Different games may have different player actions. For example, a game where players fight against vampires may support various attack moves or actions. Some of these attack actions may work well during daylight while others may work well after dark, since vampires are sensitive to light. Thus, the current time when the search is requested may affect which call-to-action elements are associated with such a game. If the current time is during the day, then one or more actions that work well against the vampires during daylight may be selected to be associated with the game. On the other hand, if the current time is evening or night, then one or more actions that work well against the vampires after dark may be selected to be associated with the game.

In particular embodiments, the available actions supported by a game may be stored on the user's device or obtained from a server (e.g., a server associated with the social-networking system or the game developer). For example, the actions may be downloaded to the user's device if the user chooses to do so. Then, from time to time, as the game is updated and new actions become available, they maybe downloaded to the user's device as needed.

As these examples suggest, by selecting a suitable or appropriate call-to-action element for a search result, it may significantly improve the user's interaction and search experience. For example, by placing a “buy tickets” call-to-action element next to a newly released movie, as described in one of the above examples, the user may buy tickets directly from the search results displayed on the user device. This saves the user the trouble of having to navigate to another website (e.g., a ticket selling website), search for the movie again, and then buy tickets. Similarly, by associating fighting moves with the web-based game, the user may play the game without having to actually go to the game's website.

In particular embodiments, most, if not all, of the relevant information may be incorporated in the graph associated with the social-networking system so that the information is readily available to the search tool for selecting and ranking search results and call-to-action elements. Each information item, big or small, may correspond to a node in the graph, and each relationship between two information item may correspond to an edge in the graph connecting the two corresponding nodes. Thus, the graph not only includes the information items but their relationships as well.

As explained above, sometimes, certain call-to-action elements may be preferred by the provider of the search tool (e.g., the social-networking system), and thus, the ranks of these preferred call-to-action elements may be boosted so that they may be selected over the other call-to-action elements to be associated with the search results. In particular embodiments, when a search result is associated with a call-to-action element preferred by the social-networking system, the rank of this search result may also be boosted so that it is presented to the user before other search results. This may increase the likelihood that the user may select the preferred call-to-action element associated with the search result.

Particular embodiments may present the search results, according to their respective ranks, to the user by display them on the user's device in the order of their ranks, as illustrated in STEP 350.

Sophisticated user devices may have a sufficient amount of storage space for storing various types data. For example, with a smart telephone, some of the user's contacts, calendar appointments, events, task lists, instant messages, emails, news feeds, etc. may be stored on the device. Such information is available even when the device is offline (i.e., disconnected from a network). In particular embodiments, the user may conduct searches with his device regardless of whether the device is online (i.e., connected to a network) or offline. If the device is online when the user requests a search, the search results may be compiled based on information both stored on the user's device as well as information available with the social-networking system and on the Internet. If the device is offline when the user requests a search, the search results may be compiled based only on the information stored on the user's device.

In particular embodiments, the search results are presented to the user (e.g., displayed on the screen of the user's electronic device) as soon as they become available. In addition, the search results may be ranked, and higher-ranked search results may be displayed on the screen of the user's electronic device above the lower-ranked search results. When a higher-ranked search result becomes available after a lower-ranked search result, it may be displayed on the screen of the user's electronic device above the lower-ranked search result, which has already been displayed, by pushing the lower-ranked search result further down on the screen of the user's electronic device.

In some cases, a user may start a search process (e.g., by submitting a search query) while the user's device is offline. However, while the search is being conducted, the device may be connected to a network and come online. In this case, before the device comes online, the search results may be obtained based on information stored locally on the device. After the device comes online, additional search results may be obtained from information available with the social-networking system and on the Internet. The new search results, after they become available, may co-mingle with the existing search results. The rank of each search result may be determined. The presentation of the search results may be adjusted when necessary to incorporate the newly available search results (e.g., inserting some newly available search results above some existing search results on the screen of the user's device).

The search tool functionalities (e.g., identifying, ranking, and boosting search results) described above may be implemented as a series of instructions stored on a computer-readable storage medium that, when executed, cause a programmable processor to implement the operations described above. FIG. 4 illustrates an example computer system 400. In particular embodiments, one or more computer systems 400 perform one or more steps of one or more methods described or illustrated herein. In particular embodiments, one or more computer systems 400 provide functionality described or illustrated herein. In particular embodiments, software running on one or more computer systems 400 performs one or more steps of one or more methods described or illustrated herein or provides functionality described or illustrated herein. Particular embodiments include one or more portions of one or more computer systems 400.

This disclosure contemplates any suitable number of computer systems 400. This disclosure contemplates computer system 400 taking any suitable physical form. As example and not by way of limitation, computer system 400 may be an embedded computer system, a system-on-chip (SOC), a single-board computer system (SBC) (such as, for example, a computer-on-module (COM) or system-on-module (SOM)), a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, or a combination of two or more of these. Where appropriate, computer system 400 may include one or more computer systems 400; be unitary or distributed; span multiple locations; span multiple machines; or reside in a cloud, which may include one or more cloud components in one or more networks. Where appropriate, one or more computer systems 400 may perform without substantial spatial or temporal limitation one or more steps of one or more methods described or illustrated herein. As an example and not by way of limitation, one or more computer systems 400 may perform in real time or in batch mode one or more steps of one or more methods described or illustrated herein. One or more computer systems 400 may perform at different times or at different locations one or more steps of one or more methods described or illustrated herein, where appropriate.

In particular embodiments, computer system 400 includes a processor 402, memory 404, storage 406, an input/output (I/O) interface 408, a communication interface 410, and a bus 412. Although this disclosure describes and illustrates a particular computer system having a particular number of particular components in a particular arrangement, this disclosure contemplates any suitable computer system having any suitable number of any suitable components in any suitable arrangement.

In particular embodiments, processor 402 includes hardware for executing instructions, such as those making up a computer program. As an example and not by way of limitation, to execute instructions, processor 402 may retrieve (or fetch) the instructions from an internal register, an internal cache, memory 404, or storage 406; decode and execute them; and then write one or more results to an internal register, an internal cache, memory 404, or storage 406. In particular embodiments, processor 402 may include one or more internal caches for data, instructions, or addresses. This disclosure contemplates processor 402 including any suitable number of any suitable internal caches, where appropriate. As an example and not by way of limitation, processor 402 may include one or more instruction caches, one or more data caches, and one or more translation lookaside buffers (TLBs). Instructions in the instruction caches may be copies of instructions in memory 404 or storage 406, and the instruction caches may speed up retrieval of those instructions by processor 402. Data in the data caches may be copies of data in memory 404 or storage 406 for instructions executing at processor 402 to operate on; the results of previous instructions executed at processor 402 for access by subsequent instructions executing at processor 402 or for writing to memory 404 or storage 406; or other suitable data. The data caches may speed up read or write operations by processor 402. The TLBs may speed up virtual-address translation for processor 402. In particular embodiments, processor 402 may include one or more internal registers for data, instructions, or addresses. This disclosure contemplates processor 402 including any suitable number of any suitable internal registers, where appropriate. Where appropriate, processor 402 may include one or more arithmetic logic units (ALUs); be a multi-core processor; or include one or more processors 402. Although this disclosure describes and illustrates a particular processor, this disclosure contemplates any suitable processor.

In particular embodiments, memory 404 includes main memory for storing instructions for processor 402 to execute or data for processor 402 to operate on. As an example and not by way of limitation, computer system 400 may load instructions from storage 406 or another source (such as, for example, another computer system 400) to memory 404. Processor 402 may then load the instructions from memory 404 to an internal register or internal cache. To execute the instructions, processor 402 may retrieve the instructions from the internal register or internal cache and decode them. During or after execution of the instructions, processor 402 may write one or more results (which may be intermediate or final results) to the internal register or internal cache. Processor 402 may then write one or more of those results to memory 404. In particular embodiments, processor 402 executes only instructions in one or more internal registers or internal caches or in memory 404 (as opposed to storage 406 or elsewhere) and operates only on data in one or more internal registers or internal caches or in memory 404 (as opposed to storage 406 or elsewhere). One or more memory buses (which may each include an address bus and a data bus) may couple processor 402 to memory 404. Bus 412 may include one or more memory buses, as described below. In particular embodiments, one or more memory management units (MMUs) reside between processor 402 and memory 404 and facilitate accesses to memory 404 requested by processor 402. In particular embodiments, memory 404 includes random access memory (RAM). This RAM may be volatile memory, where appropriate. Where appropriate, this RAM may be dynamic RAM (DRAM) or static RAM (SRAM). Moreover, where appropriate, this RAM may be single-ported or multi-ported RAM. This disclosure contemplates any suitable RAM. Memory 404 may include one or more memories 404, where appropriate. Although this disclosure describes and illustrates particular memory, this disclosure contemplates any suitable memory.

In particular embodiments, storage 406 includes mass storage for data or instructions. As an example and not by way of limitation, storage 406 may include an HDD, a floppy disk drive, flash memory, an optical disc, a magneto-optical disc, magnetic tape, or a Universal Serial Bus (USB) drive or a combination of two or more of these. Storage 406 may include removable or non-removable (or fixed) media, where appropriate. Storage 406 may be internal or external to computer system 400, where appropriate. In particular embodiments, storage 406 is non-volatile, solid-state memory. In particular embodiments, storage 406 includes read-only memory (ROM). Where appropriate, this ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically alterable ROM (EAROM), or flash memory or a combination of two or more of these. This disclosure contemplates mass storage 406 taking any suitable physical form. Storage 406 may include one or more storage control units facilitating communication between processor 402 and storage 406, where appropriate. Where appropriate, storage 406 may include one or more storages 406. Although this disclosure describes and illustrates particular storage, this disclosure contemplates any suitable storage.

In particular embodiments, I/O interface 408 includes hardware, software, or both providing one or more interfaces for communication between computer system 400 and one or more I/O devices. Computer system 400 may include one or more of these I/O devices, where appropriate. One or more of these I/O devices may enable communication between a person and computer system 400. As an example and not by way of limitation, an I/O device may include a keyboard, keypad, microphone, monitor, mouse, printer, scanner, speaker, still camera, stylus, tablet, touch screen, trackball, video camera, another suitable I/O device or a combination of two or more of these. An I/O device may include one or more sensors. This disclosure contemplates any suitable I/O devices and any suitable I/O interfaces 408 for them. Where appropriate, I/O interface 408 may include one or more device or software drivers enabling processor 402 to drive one or more of these I/O devices. I/O interface 408 may include one or more I/O interfaces 408, where appropriate. Although this disclosure describes and illustrates a particular I/O interface, this disclosure contemplates any suitable I/O interface.

In particular embodiments, communication interface 410 includes hardware, software, or both providing one or more interfaces for communication (such as, for example, packet-based communication) between computer system 400 and one or more other computer systems 400 or one or more networks. As an example and not by way of limitation, communication interface 410 may include a network interface controller (NIC) or network adapter for communicating with an Ethernet or other wire-based network or a wireless NIC (WNIC) or wireless adapter for communicating with a wireless network, such as a WI-FI network. This disclosure contemplates any suitable network and any suitable communication interface 410 for it. As an example and not by way of limitation, computer system 400 may communicate with an ad hoc network, a personal area network (PAN), a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), or one or more portions of the Internet or a combination of two or more of these. One or more portions of one or more of these networks may be wired or wireless. As an example, computer system 400 may communicate with a wireless PAN (WPAN) (such as, for example, a BLUETOOTH WPAN), a WI-FI network, a WI-MAX network, a cellular telephone network (such as, for example, a Global System for Mobile Communications (GSM) network), or other suitable wireless network or a combination of two or more of these. Computer system 400 may include any suitable communication interface 410 for any of these networks, where appropriate. Communication interface 410 may include one or more communication interfaces 410, where appropriate. Although this disclosure describes and illustrates a particular communication interface, this disclosure contemplates any suitable communication interface.

In particular embodiments, bus 412 includes hardware, software, or both coupling components of computer system 400 to each other. As an example and not by way of limitation, bus 412 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a front-side bus (FSB), a HYPERTRANSPORT (HT) interconnect, an Industry Standard Architecture (ISA) bus, an INFINIBAND interconnect, a low-pin-count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a serial advanced technology attachment (SATA) bus, a Video Electronics Standards Association local (VLB) bus, or another suitable bus or a combination of two or more of these. Bus 412 may include one or more buses 412, where appropriate. Although this disclosure describes and illustrates a particular bus, this disclosure contemplates any suitable bus or interconnect.

Herein, reference to a computer-readable storage medium encompasses one or more non-transitory, tangible computer-readable storage media possessing structure. As an example and not by way of limitation, a computer-readable storage medium may include a semiconductor-based or other integrated circuit (IC) (such, as for example, a field-programmable gate array (FPGA) or an application-specific IC (ASIC)), a hard disk, an HDD, a hybrid hard drive (HHD), an optical disc, an optical disc drive (ODD), a magneto-optical disc, a magneto-optical drive, a floppy disk, a floppy disk drive (FDD), magnetic tape, a holographic storage medium, a solid-state drive (SSD), a RAM-drive, a SECURE DIGITAL card, a SECURE DIGITAL drive, or another suitable computer-readable storage medium or a combination of two or more of these, where appropriate. Herein, reference to a computer-readable storage medium excludes any medium that is not eligible for patent protection under 35 U.S.C. §101. Herein, reference to a computer-readable storage medium excludes transitory forms of signal transmission (such as a propagating electrical or electromagnetic signal per se) to the extent that they are not eligible for patent protection under 35 U.S.C. §101. A computer-readable non-transitory storage medium may be volatile, non-volatile, or a combination of volatile and non-volatile, where appropriate.

This disclosure contemplates one or more computer-readable storage media implementing any suitable storage. In particular embodiments, a computer-readable storage medium implements one or more portions of processor 402 (such as, for example, one or more internal registers or caches), one or more portions of memory 404, one or more portions of storage 406, or a combination of these, where appropriate. In particular embodiments, a computer-readable storage medium implements RAM or ROM. In particular embodiments, a computer-readable storage medium implements volatile or persistent memory. In particular embodiments, one or more computer-readable storage media embody software. Herein, reference to software may encompass one or more applications, bytecode, one or more computer programs, one or more executables, one or more instructions, logic, machine code, one or more scripts, or source code, and vice versa, where appropriate. In particular embodiments, software includes one or more application programming interfaces (APIs). This disclosure contemplates any suitable software written or otherwise expressed in any suitable programming language or combination of programming languages. In particular embodiments, software is expressed as source code or object code. In particular embodiments, software is expressed in a higher-level programming language, such as, for example, C, Perl, or a suitable extension thereof. In particular embodiments, software is expressed in a lower-level programming language, such as assembly language (or machine code). In particular embodiments, software is expressed in JAVA, C, or C++. In particular embodiments, software is expressed in Hyper Text Markup Language (HTML), Extensible Markup Language (XML), or other suitable markup language.

Herein, “or” is inclusive and not exclusive, unless expressly indicated otherwise or indicated otherwise by context. Therefore, herein, “A or B” means “A, B, or both,” unless expressly indicated otherwise or indicated otherwise by context. Moreover, “and” is both joint and several, unless expressly indicated otherwise or indicated otherwise by context. Therefore, herein, “A and B” means “A and B, jointly or severally,” unless expressly indicated otherwise or indicated otherwise by context.

This disclosure encompasses all changes, substitutions, variations, alterations, and modifications to the example embodiments herein that a person having ordinary skill in the art would comprehend. Similarly, where appropriate, the appended claims encompass all changes, substitutions, variations, alterations, and modifications to the example embodiments herein that a person having ordinary skill in the art would comprehend. Moreover, reference in the appended claims to an apparatus or system or a component of an apparatus or system being adapted to, arranged to, capable of, configured to, enabled to, operable to, or operative to perform a particular function encompasses that apparatus, system, component, whether or not it or that particular function is activated, turned on, or unlocked, as long as that apparatus, system, or component is so adapted, arranged, capable, configured, enabled, operable, or operative. 

What is claimed is:
 1. A method comprising, by one or more computing devices of an online social network: receiving, at the one or more computing devices via a search tool provided at a client device of a first user of the online social network, a search query for content items of the online social network; identifying, by the one or more computing devices, one or more content items of the online social network, respectively, that match the search query, wherein the one or more identified content items correspond one or more nodes of a social graph, respectively; sending, from the one or more computing devices to the client device for presentation to the first user in response to the search query, one or more search results corresponding to one or more of the identified content items, respectively, each search result being presented with one or more call-to-action elements applicable to the content item, each call-to-action element being a user-interface element that prompts an action from the first user to initiate a particular function or process related to the content item via the online social network, the call-to-action elements being presented in ranked order based on a suitability of the call-to-action element to the content item; receiving, at the one or more computing devices from the client device of the first user, a selection of a call-to-action element presented with a particular search result corresponding to a particular content item; and executing, by the one or more computing devices, the function or process related to the particular content item on the online social network prompted by the selected call-to-action element, wherein the function or process related to the particular content item comprises generating an edge connected to the node corresponding to the particular content item.
 2. The method of claim 1, further comprising: accessing the social graph, wherein the social graph comprises a plurality of nodes and a plurality of edges connecting the nodes, wherein the first user corresponds to a first node of the plurality of nodes, and wherein the one or more identified content items correspond to one or more second nodes, respectively, of the plurality of nodes.
 3. The method of claim 2, wherein the call-to-action elements being presented in ranked order further based on at least one relationship in the social graph between the first node corresponding to the first user and the second nodes corresponding to the content item of the search result.
 4. The method of claim 2, wherein at least one edge in the social graph is based at least in part on the first user's past behavior with respect to an identified content item corresponding to one of the second nodes of the social graph.
 5. The method of claim 1, further comprising: identifying, for each identified content item, a plurality of call-to-action elements applicable to the content item; and ranking, for each identified content item, the call-to-action elements applicable to the content item based a suitability of the call-to-action element to the content item.
 6. The method of claim 1, wherein the call-to-action elements are presented to the user in proximity to their corresponding search results.
 7. The method of claim 1, wherein, for each search results, at least one of the call-to-action elements is presented as a top-ranked call-to-action element.
 8. The method of claim 1, wherein, for each search result, at least one of the call-to-action elements prompts an action that is convenient for the user to perform with respect to the content item corresponding to the search result.
 9. The method of claim 1, wherein, for each search result, at least one of the call-to-action elements corresponds to an action that the online social network encourages the user to perform with respect to the content item corresponding to the search result.
 10. The method of claim 1, wherein at least one of the call-to-action elements corresponds to an action that is likely to be performed by the user with respect to the content item corresponding to the search result.
 11. The method of claim 1, wherein the call-to-action elements being presented in ranked order further based on one or more of: the nature and context of the search query, social or demographical information of the first user, a behavior profile of the first user, a time and location associated with the first user when the search query is received, or one or more business objectives of the online social network.
 12. The method of claim 1, wherein the search query comprises one or more characters inputted by the first user, the one or more characters being received as the first user inputs the one or more characters at the client device into a user interface of the online social network.
 13. The method of claim 1, further comprising: ranking each of the identified content items based on a historical behavior of the user in selecting one or more prior search results.
 14. The method of claim 13, further comprising: boosting one or more ranks of one or more of the identified content items based on a business objective of the social-networking system.
 15. The method of claim 1, wherein the one or more search results are sent for presentation to the first user as the first user inputs one or more characters of the search query at the client device into a user interface of the online social network, the presentation of the search results to the first user enabling the first user to select one or more of the search results to interact with.
 16. The method of claim 1, wherein the search query is a user-generated character string comprising the one or more characters inputted by the first user, the user-generated character string being received from the client device of the first user, and wherein the user-generated character string is entered by the first user in a query field and rendered at the client device as each character of the character string is entered by the first user.
 17. The method of claim 1, further comprising auto-populating, at the client device, a query field with the search results.
 18. The method of claim 1, wherein the search results are presented as real-time suggestions responsive to one or more characters thus far inputted by the first user at the client device into a user interface of the online social network.
 19. One or more computer-readable non-transitory storage media embodying software that is operable when executed by one or more computing devices of an online social network to: receive, at the one or more computing devices via a search tool provided at a client device of a first user of the online social network, a search query for content items of the online social network; identify, by the one or more computing devices, one or more content items of the online social network, respectively, that match the search query, wherein the one or more identified content items correspond one or more nodes of a social graph, respectively; send, from the one or more computing devices to the client device for presentation to the first user in response to the search query, one or more search results corresponding to one or more of the identified content items, respectively, each search result being presented with one or more call-to-action elements applicable to the content item, each call-to-action element being a user-interface element that prompts an action from the first user to initiate a particular function or process related to the content item via the online social network, the call-to-action elements being presented in ranked order based on a suitability of the call-to-action element to the content item; receive, at the one or more computing devices from the client device of the first user, a selection of a call-to-action element presented with a particular search result corresponding to a particular content item; and execute, by the one or more computing devices, the function or process related to the particular content item on the online social network prompted by the selected call-to-action element, wherein the function or process related to the particular content item comprises generating an edge connected to the node corresponding to the particular content item.
 20. A system comprising: one or more processors; and a non-transitory memory coupled to the processors comprising instructions executable by the processors, the processors operable when executing the instructions to: receive, at one or more computing devices of an online social network via a search tool provided at a client device of a first user of the online social network, a search query for content items of the online social network; identify, by the one or more computing devices, one or more content items of the online social network, respectively, that match the search query, wherein the one or more identified content items correspond one or more nodes of a social graph, respectively; send, from the one or more computing devices to the client device ef for presentation to the first user in response to the search query, one or more search results corresponding to one or more of the identified content items, respectively, each search result being presented with one or more call-to-action elements applicable to the content item, each call-to-action element being a user-interface element that prompts an action from the first user to initiate a particular function or process related to the content item via the online social network, the call-to-action elements being presented in ranked order based on a suitability of the call-to-action element to the content item; receive, at the one or more computing devices from the client device of the first user, a selection of a call-to-action element presented with a particular search result corresponding to a particular content item; and execute, by the one or more computing devices, the function or process related to the particular content item on the online social network prompted by the selected call-to-action element, wherein the function or process related to the particular content item comprises generating an edge connected to the node corresponding to the particular content item.
 21. The method of claim 1, wherein the ranked order of the one or more call-to-action elements applicable to a content item is further based on at least one relationship between the first user and the content item.
 22. The method of claim 1, wherein the search query comprises a character string inputted by the first user. 